In [1]:
#pipeline for advanced lane finding:
#1.- Camera calibration
#Camera calibration
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
import pickle
%matplotlib inline
images = glob.glob("camera_cal/calibration*.jpg")
objpoints = []
imgpoints = []
nx = 9 # The number of inside corners in x
ny = 6 # The number of inside corners in y
objp = np.zeros((ny*nx,3), np.float32)
objp[:,:2] = np.mgrid[0:nx,0:ny].T.reshape(-1,2)
for fname in images:
img = mpimg.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx,ny), None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
# Draw and display the corners
chess_corn = cv2.drawChessboardCorners(img, (nx,ny), corners, ret)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20,10))
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(chess_corn)
ax2.set_title('Corners Image', fontsize=40)
Out[1]:
In [2]:
#2.- undistord image
img_size = (img.shape[1], img.shape[0])
# Do camera calibration given object points and image points
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, img_size,None,None)
dst = cv2.undistort(img, mtx, dist, None, mtx)
dist_pickle = {}
dist_pickle["mtx"] = mtx
dist_pickle["dist"] = dist
pickle.dump( dist_pickle, open( "wide_dist_pickle.p", "wb" ) )
#dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
# Visualize undistortion
fname = 'test_images/test1.jpg'
image = mpimg.imread(fname)
result = cv2.undistort(image, mtx, dist, None, mtx)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(result, cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [3]:
#3.- functions of interest
import math
def grayscale(img):
"""Applies the Grayscale transform
This will return an image with only one color channel
but NOTE: to see the returned image as grayscale
(assuming your grayscaled image is called 'gray')
you should call plt.imshow(gray, cmap='gray')"""
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Or use BGR2GRAY if you read an image with cv2.imread()
# return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
def hls_s(img):
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
return s_channel
def gaussian_blur(img, kernel_size):
"""Applies a Gaussian Noise kernel"""
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
def region_of_interest(img, vertices):
"""
Applies an image mask.
Only keeps the region of the image defined by the polygon
formed from `vertices`. The rest of the image is set to black.
"""
#defining a blank mask to start with
mask = np.zeros_like(img)
#defining a 3 channel or 1 channel color to fill the mask with depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
#filling pixels inside the polygon defined by "vertices" with the fill color
cv2.fillPoly(mask, vertices, ignore_mask_color)
#returning the image only where mask pixels are nonzero
masked_image = cv2.bitwise_and(img, mask)
return masked_image
In [4]:
# Calculate directional gradient
def abs_sobel_thresh(gray, orient='x', sobel_kernel=3, thresh=(0, 255)):
# Apply x or y gradient
if orient == 'x':
sobel = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
elif orient == 'y':
sobel = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Take the absolute values
sobel = np.absolute(sobel)
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*sobel/np.max(sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel > thresh[0]) & (scaled_sobel < thresh[1])] = 1
# Return the result
return binary_output
# Calculate gradient magnitude
def mag_thresh(gray, sobel_kernel=3, mag_thresh=(0, 255)):
# Apply x and y gradients
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
# Calculate the gradient magnitude
sobel = np.sqrt(sobelx ** 2 + sobely ** 2)
# Rescale back to 8 bit integer
scaled_sobel = np.uint8(255*sobel/np.max(sobel))
# Create a copy and apply the threshold
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel > mag_thresh[0]) & (scaled_sobel < mag_thresh[1])] = 1
# Return the result
return binary_output
def dir_threshold(image, sobel_kernel=3, thresh=(0, np.pi/2)):
# Apply the following steps to img
# 2) Take the gradient in x and y separately
sobelx = cv2.Sobel(gray, cv2.CV_64F , 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F , 0, 1, ksize=sobel_kernel)
# 3) Take the absolute value of the x and y gradients
# 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient
absgraddir = np.arctan(np.absolute(sobely), np.absolute(sobelx))
# 5) Create a binary mask where direction thresholds are met
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
# 6) Return this mask as your binary_output image
return binary_output
def paint_lines(img, corners):
corner_tuples=[]
for ind,c in enumerate(corners):
corner_tuples.append(tuple(corners[ind]))
#cv2.polylines(img, corners, True, (0,255,0,0), 1)
cv2.line(img, corner_tuples[0], corner_tuples[1], color=[255,0,0], thickness=2)
cv2.line(img, corner_tuples[1], corner_tuples[2], color=[255,0,0], thickness=2)
cv2.line(img, corner_tuples[2], corner_tuples[3], color=[255,0,0], thickness=2)
cv2.line(img, corner_tuples[3], corner_tuples[0], color=[255,0,0], thickness=2)
In [5]:
# Pipeline:
def pipeline(img):
img = gaussian_blur(img, 5)
s_channel = hls_s(img)
gray = grayscale(img)
ksize = 7
# Apply each of the thresholding functions
gradx = abs_sobel_thresh(gray, orient='x', sobel_kernel=ksize, thresh=(40, 255))
grady = abs_sobel_thresh(gray, orient='y', sobel_kernel=ksize, thresh=(60, 255))
mag_binary = mag_thresh(gray, sobel_kernel=ksize, mag_thresh=(40, 255))
dir_binary = dir_threshold(gray, sobel_kernel=ksize, thresh=(.8, 1.2))
# Combine all the thresholding
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
# Threshold color channel
s_binary = np.zeros_like(combined)
s_binary[(s_channel > 160) & (s_channel < 255)] = 1
# Stack each channel to view their individual contributions in green and blue respectively
# This returns a stack of the two binary images, whose components you can see as different colors
color_binary = np.zeros_like(combined)
color_binary[(s_binary > 0) | (combined > 0)] = 1
#corners
corners = np.array([[130,720],[589,457],[750,457],[1200,720]], dtype=np.int32)
# Masked area
vertices = np.array([[(130, 720), (589, 457), (750, 457), (1200, 720)]], dtype=np.int32)
color_binary = region_of_interest(color_binary, vertices)
paint_lines(image, corners)
return color_binary
fname = 'test_images/test1.jpg'
image = mpimg.imread(fname)
result = pipeline(image)
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(image)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(result, cmap='gray')
ax2.set_title('Pipeline Result', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [6]:
#5.- transform the region of interest
area_of_interest = [[589,457],[750,457],[1200,720],[145,720]]
def corners_unwarp(img, mtx, dist):
# Pass in your image into this function
# Write code to do the following steps
# 1) Undistort using mtx and dist
undistord = cv2.undistort(img, mtx, dist, None, mtx)
offset = 150
offset2 = 0
img_size = (img.shape[1], img.shape[0])
src = np.float32(area_of_interest)
# c) define 4 destination points dst = np.float32([[,],[,],[,],[,]])
dst = np.float32([[offset, offset2],
[img_size[0]-offset, offset2],
[img_size[0]-offset, img_size[1]-offset2],
[offset, img_size[1]-offset2]])
# d) use cv2.getPerspectiveTransform() to get M, the transform matrix
M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
# e) use cv2.warpPerspective() to warp your image to a top-down view
warped = cv2.warpPerspective(img, M, img_size, flags=cv2.INTER_LINEAR)
#delete the next two lines
return warped, M, Minv
# Plot the result
fname = 'test_images/test1.jpg'
image = mpimg.imread(fname)
result = pipeline(image)
warped, M, Minv = corners_unwarp(result, mtx, dist)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(result, cmap='gray')
ax1.set_title('Pipeline Result', fontsize=40)
ax2.imshow(warped, cmap='gray')
ax2.set_title('Warped Result', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [7]:
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = [np.array([False])]
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#x values for detected line pixels
self.allx = None
#y values for detected line pixels
self.ally = None
#x values in windows
self.windows = np.ones((3,12))*-1
# always the same y-range as image
self.fit_yvals = np.linspace(0, 100, num=101)*7.2
# last value
self.last_value = None
In [8]:
#TODO sanity check
def sanity_check(line, curvature, fitx, fit):
if line.detected:
if (abs(curvature / line.radius_of_curvature -1) < 0.5) and ():
line.bestx = np.mean(fitx)
line.current_fit = fit
line.radius_of_curvature = curvature
line.allx = fitx
else:
if (curvature > line.radius_of_curvature):
line.detected = False
fitx = line.allx
else:
# If lane was not detected and no curvature is defined
if line.radius_of_curvature:
if abs(curvature / line.radius_of_curvature - 1) < 1:
line.detected = True
line.current_fit = fit
line.allx = fitx
line.bestx = np.mean(fitx)
line.radius_of_curvature = curvature
line.current_fit = fit
else:
fitx = line.allx
# If curvature was defined
else:
line.detected = True
line.current_fit = fit
line.allx = fitx
line.bestx = np.mean(fitx)
line.radius_of_curvature = curvature
return fitx
def distance_sanity_check(left, right, leftx_fit, rightx_fit):
if left.detected and right.detected:
print(abs(left.last_value - right.last_value))
else:
print(abs(leftx_fitx, rightx_fit))
In [9]:
#If lanes not detected:
def find_lanes(binary_warped):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
return left_fitx, right_fitx, ploty, left_fit, right_fit, leftx, rightx
def fit_lines(binary_warped, left_fit, right_fit):
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
return left_fitx, right_fitx, ploty, left_fit, right_fit, leftx, rightx
fname = 'test_images/test1.jpg'
image = mpimg.imread(fname)
result = pipeline(image)
warped, M, Minv = corners_unwarp(result, mtx, dist)
left = Line()
right = Line()
left_fitx, right_fitx, ploty, left_fit, right_fit, leftx, rightx = find_lanes(warped)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
ax1.imshow(result, cmap='gray')
ax1.set_title('Pipeline Result', fontsize=40)
ax2.imshow(warped, cmap='gray')
ax2.set_title('Warped Result', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [10]:
# Define conversions in x and y from pixels space to meters
ym_per_pix = 30/720 # meters per pixel in y dimension
xm_per_pix = 3.7/700 # meters per pixel in x dimension
def get_curvature(ploty, leftx, rightx):
y_eval = np.max(ploty)
# Fit new polynomials to x,y in world space
left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
# Calculate the new radii of curvature
left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
return left_curverad, right_curverad
def position(img, pts, center_pre):
initial = img.shape[1]/2
left_pts = pts[(pts[:,1]<initial)&(pts[:,0]>img.shape[0]-20)][:,1]
right_pts = pts[(pts[:,1]>initial)&(pts[:,0]>img.shape[0]-20)][:,1]
if (len(right_pts) == 0):
center = center_pre
else:
left = np.min(left_pts)
right = np.max(right_pts)
center = (left+right)/2
return (initial-center)*xm_per_pix
In [11]:
# Create an image to draw the lines on
def draw_lane(warped, left_fitx, right_fitx, ploty, Minv, undist, curvature, car_position):
warp_zero = np.zeros_like(warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
# Recast the x and y points into usable format for cv2.fillPoly()
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
# Draw the lane onto the warped blank image
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
# Warp the blank back to original image space using inverse perspective matrix (Minv)
newwarp = cv2.warpPerspective(color_warp, Minv, (image.shape[1], image.shape[0]))
# Combine the result with the original image
result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)
font = cv2.FONT_HERSHEY_SIMPLEX
text = "Radius of Curvature: {} m".format(int(curvature))
cv2.putText(result,text,(400,100), font, 1,(255,255,255),2)
pts = np.argwhere(newwarp[:,:,1])
car_position_pre = car_position
car_position = position(result,pts, car_position_pre)
if car_position < 0:
text = "Vehicle is {:.2f} m left of center".format(-car_position)
else:
text = "Vehicle is {:.2f} m right of center".format(car_position)
cv2.putText(result,text,(400,150), font, 1,(255,255,255),2)
return result
#for i in range(1,7):
fname = 'test_images/test1.jpg'
image = mpimg.imread(fname)
undist = cv2.undistort(image, mtx, dist, None, mtx)
result = pipeline(undist)
warped, M, Minv = corners_unwarp(result, mtx, dist)
left = Line()
right = Line()
left_fitx, right_fitx, ploty, left_fit, right_fit, leftx, rightx = find_lanes(warped)
left_curvature, right_curvature = get_curvature(ploty, left_fitx, right_fitx)
car_position = 0
image_fin = draw_lane(warped, left_fitx, right_fitx, ploty, Minv, undist, left_curvature, car_position)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(undist)
ax1.set_title('Original Image', fontsize=40)
ax2.imshow(image_fin)
ax2.set_title('Pipeline Result', fontsize=40)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [12]:
def def_pipeline(image, left, right, car_position):
undist = cv2.undistort(image, mtx, dist, None, mtx)
result = pipeline(undist)
warped, M, Minv = corners_unwarp(result, mtx, dist)
if (left.detected == False) and (right.detected ==False):
left_fitx, right_fitx, ploty, left_fit, right_fit, leftx, rightx = find_lanes(warped)
else:
left_fitx, right_fitx, ploty, left_fit, right_fit, leftx, rightx = fit_lines(warped, left.current_fit, right.current_fit)
left_curvature, right_curvature = get_curvature(ploty, left_fitx, right_fitx)
left_fitx = sanity_check(left, left_curvature, left_fitx, left_fit)
right_fitx = sanity_check(right,right_curvature, right_fitx, right_fit)
result = draw_lane(warped, left_fitx, right_fitx, ploty, Minv, undist, left_curvature, car_position)
return result
In [13]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
left = Line()
right = Line()
left_fit, right_fit = [], []
car_position = 0
def process_image(image):
image_fin = def_pipeline(image, left,right, car_position)
return image_fin
In [14]:
white_output = 'project.mp4'
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
In [15]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(white_output))
Out[15]:
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